Face authentication using enhanced fisher linear discriminant model (EFM)

Authors:
Djamel Saigaa;N. Benoudjit;K. Benmahamed;S. Lelendais
Affiliations:
Automatics Department, University Mohamed Khider, Biskra, Algeria;Electronics Department, University of Batna, Algeria;Electronics Department, University of Setif, Algeria;Complex Systems Laboratory, University of Evry Val Essonne, French
Venue:
CIMMACS'05 Proceedings of the 4th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Year:
2005

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Abstract

In this paper, Enhanced Fisher linear discriminant Model (EFM) is presented as an alternative feature extraction algorithm to Principal Component Analysis (PCA) widely used in automatic face recognition/authentication tasks. We show that the promising EFM algorithm extracts from faces features that are relevant and efficient for authentication. This leads to improved success rates and a reduced client model size over a PCA based feature extraction. The feasibility of the EFM method has been successfully tested on face authentication using 2360 XM2VTS frontal face images corresponding to 295 subjects, which were acquired under variable illumination and facial expressions. By the EFM method we obtain an equal error rate of 1.96% on face authentication using only 56 features.